Fatty acid fingerprints in bronchoalveolar lavage fluid and its extracellular vesicles reflect equine asthma severity

Equine asthma (EA) is an inflammatory disease of the lower airways driven by mediators released from cells. Extracellular vesicles (EVs) are vehicles for lipid mediators, which possess either pro-inflammatory or dual anti-inflammatory and pro-resolving functions. In this study, we investigated how the respiratory fatty acid (FA) profile reflects airway inflammatory status. The FA composition of bronchoalveolar lavage fluid (BALF), BALF supernatant, and bronchoalveolar EVs of healthy horses (n = 15) and horses with mild/moderate EA (n = 10) or severe EA (SEA, n = 5) was determined with gas chromatography and mass spectrometry. The FA profiles distinguished samples with different diagnoses in all sample types, yet they were insufficient to predict the health status of uncategorized samples. Different individual FAs were responsible for the discrimination of the diagnoses in different sample types. Particularly, in the EVs of SEA horses the proportions of palmitic acid (16:0) decreased and those of eicosapentaenoic acid (20:5n-3) increased, and all sample types of asthmatic horses had elevated dihomo-γ-linolenic acid (20:3n-6) proportions. The results suggest simultaneous pro-inflammatory and resolving actions of FAs and a potential role for EVs as vehicles for lipid mediators in asthma pathogenesis. EV lipid manifestations of EA can offer translational targets to study asthma pathophysiology and treatment options.


Materials and methods
Animals, sample collection, and preparation. Privately owned horses (n = 12) and ponies (n = 3) with naturally occurring EA, and healthy control horses (n = 13) and ponies (n = 2) were enrolled in this prospective clinical case-control study conducted at the Faculty of Veterinary Medicine, University of Helsinki. The animal experiment was approved by the Project Authorisation Board (ESAVI/3285/2020), and all experiments were performed in accordance with EU regulations and ARRIVE guidelines 24 . The owners provided a written informed consent prior to the participation. The horses were fed an unstandardized hay/haylage-based diet. Information on the use of specific FA supplements was not available. Inclusion criteria were as follows: no signs of infection during the last two months and no medication during the last month. Exclusion criteria included abnormalities in physical examinations (purulent nasal discharge, enlarged submandibular lymph nodes, fever, diarrhea, poor body condition), abnormal blood analyses (blood leukocyte or serum amyloid A concentration outside reference ranges), and abnormalities in airway endoscopy (laryngeal dysfunction, arytenoid chondropathy, pneumonia, neoplasia). Horses were diagnosed as asthmatic based on patient history (recurrent or chronic signs of sterile inflammatory lung disease) and neutrophilia (neutrophil-% of 5-25% for MMEA and > 25% for SEA) in BALF cytology. Horses were diagnosed as healthy based on patient history (no signs of recurrent or chronic lung disease in known history) and lack of neutrophilia (neutrophil-% of < 5%) in BALF cytology 25 . Only horses with neutrophilic form of asthma were included in the study 26 .
FA determination. FA analysis of BALF, SUP, and EVs was performed at the Helsinki University Lipidomics Unit using gas chromatography with flame ionization and mass spectrometric detection as outlined previously 28 . FAs were released from lipids and converted to FA methyl esters (FAMEs) in a transmethylation reaction in methanolic H 2 SO 4 under nitrogen atmosphere. The formed FAMEs were then extracted with hexane and analyzed by Shimadzu GC-2010 Plus gas chromatograph with flame ionization detector (Shimadzu, Kyoto, Japan). The FAME structures were verified with electron impact mass spectra recorded by the Shimadzu GCMS-QP2010 Ultra with a mass selective detector. The gas chromatographic peak representing docosahexaenoic acid (22:6n-3) also included an artefact, but this peak was regardless included in the analyses, as the artefact was not the major component of the peak, and it was estimated to be present with an equal proportion in all samples. The FA composition is presented as molar percentages (mol-%).

Conventional statistics and discriminant analysis (DA). Univariate statistical analyses and the DA
were performed using the IBM SPSS v27 software (IBM, Armonk, NY, USA). To test for differences between the diagnosis groups, the Kruskal-Wallis test was used for body weight, trachea mucus score, arterial oxygen content, and cytology, the Fisherʼs exact test for sex distribution, and the one-way analysis of variance for age. Comparisons of FA composition between the diagnosis groups and sample types were performed with the generalized linear model, in which the variables showing significance were compared post hoc all groups pairwise. The Studentʼs t-test was used for comparisons of FA profiles between controls and pooled data of all asthmatic horses in different sample types. Correlations were calculated with the Spearman correlation coefficient (r s ). The p < 0.05 was considered statistically significant and results are presented as the mean ± SE. In addition to univariate analyses, a range of multivariate analyses (see below) using both sample type-specific and combined datasets were performed to explore the data from complementary, non-overlapping angles. Those analyses were performed using FA composition data, and it is indicated below when individual FA proportions were supplemented with FA sums and derived indices, which were calculated as described previously 28 . The supervised DA was used to assess whether the diagnosis groups differed in the whole FA profile and which sample type best classified the samples into their respective groups. For cross-validation, we performed a leave-one-out strategy, to get insight on whether the obtained discriminant functions could correctly classify uncategorized samples 29 . Soft independent modeling of class analogy (SIMCA). We used Principal Component Analysis (PCA, an unsupervised multivariate method) followed by SIMCA 30 to test quantitatively, comparing two diagnosis groups at a time, whether the diagnosis groups had statistically different FA compositions. Arcsin-transformed and standardized FA mol-% data, i.e., with homogenized variable deviations, were used. The analyses were performed with the Sirius v8.5 software (Pattern Recognition Systems, Bergen, Norway).
Hierarchical clustering (HC) and correlation analysis. For each FA or derived index when indicated, the measured abundance was converted to a Z-score (normalization across samples) using the IBM SPSS. Z-score data were then loaded onto ClustVis (https:// biit. cs. ut. ee/ clust vis/) and HC was performed using "correlations" as the clustering distance and the Ward clustering method 31 for both rows (FA Z-scores) and columns (samples). Sample type and diagnosis were not used for clustering. Pearson (linear) correlation analysis was performed in R v4.1.2 (Team R 2020) using the corrplot library. FA clustering into 6 biologically relevant distinct groups was assessed visually from the correlogram and confirmed, in large part, from the (unsupervised) HC analysis.
First, we probed the capacity of the RF model to recognize a sample with a known diagnosis based on different subsets of correlated FA variables. Since 6 groups of highly correlated FA variables (scores) were identified, the original dataset of 89 samples was enriched by creating reduced datasets of 6 features only, but with a 1000-fold number of samples obtained by randomly sampling one FA score from each group only once to create samples with 6 features for classification instead of including the scores of each FA. The dataset was then split into training www.nature.com/scientificreports/ and testing data by randomly selecting 80% of the enriched samples into a training set and 20% into a testing set. Second, we probed the capacity of the model to predict diagnosis of samples without including supervising information on diagnoses of the training samples. In this approach, the original data were split before enrichment into the training and testing sets and only then we performed the 1000-fold enrichment separately on these sets. The relevance of individual FAs or FA groups in estimating the diagnosis was quantified using sklearn's feature_importances_function, and estimated from the training data only. We note that the prediction accuracy of the model, based on mean decrease in impurity, remained imperfect in the current study (see Results). Hence, it is possible that relevant FAs or FA groups were underestimated in the analysis. The RF prediction accuracy scores were defined based on averages of 20 leave-one-out RF runs.
Ethics statement. The animal study was reviewed and approved by the Project Authorization Board in the Regional State Administrative Agency (ESAVI/3285/2020). Written informed consent was obtained from the owners for the participation of their animals in this study.

Results
Clinical variables. The general characteristics of the horses are described in Table 1. There were no differences in the average age, body weight, sex ratio, or arterial oxygen content between the groups, while the mucus score was significantly elevated in MMEA compared to control. Prevalent symptoms in EA horses reported by the owners included cough (n = 13), poor performance (n = 7), and abdominal breathing pattern (n = 9). Out of 15 horses with EA, 10 horses were diagnosed with MMEA (BALF neutrophils 11.3 ± 1.47%), and 5 horses with SEA (39.1 ± 5.76%). Typical clinical findings in EA horses were increased respiratory rate (n = 10), abdominal breathing pattern (n = 9), and abnormal respiratory auscultation (n = 8). Control horses had no abnormal clinical findings and the mean BALF neutrophil-% was 1.9 ± 0.24%.   , and dihomolinoleic acid (20:2n-6) increased in SEA, and n-3/n-6 PUFA ratios and product/precursor ratios of both n-3 and n-6 PUFAs were elevated (Supplementary Table 1). In contrast, 16:1n-9 and ∆5-DIs were lower in SEA. The only significant difference in MMEA was the decreased proportion of 17:0ai compared to controls. When all asthmatic horses were grouped together and compared to controls, many of the above-mentioned differences remained significant (data not shown). In addition ,  SUP. MMEA was characterized with elevated proportions of several FAs with minor proportions when compared to controls, including 20:0, 20:3n-6, 22:5n-3, and 24:0, while the differences between control and SEA did not reach significance (Supplementary Table 2). ∆5-DIs were reduced and product/precursor ratios of n-6 PUFAs elevated in MMEA. When the pooled asthma samples were compared to controls, most of the abovementioned differences remained significant and 20:4n-6, 20:5n-3, and n-3/n-6 PUFA ratios also increased in EA (Fig. 3).

Discriminant analysis.
In order to see if the FA differences were sufficient to distinguish sample types, we performed DA where the BALF FA profiles were separated from the corresponding SUP and EVs, which aligned closer to each other ( Supplementary Fig. 1). The analysis classified 95.5% of samples correctly into their respective groups. The FAs with the largest separation power included 16:1n-9 (function 1), 20:1n-11, 20:1n-7, gondoic acid (20:1n-9), and 18:1n-9 (function 2).
In order to test which sample type provides the best separation power in FA analysis, the DA was repeated on BALF, SUP, and EV samples separately, with the diagnosis as the grouping variable (Fig. 5). In BALF, the analysis classified 100% of the samples into their correct diagnosis group. The variables with the largest separation power included palmitvaccenic acid (16:1n-5), pentadecylic acid (15:0), 18:1n-7, and isopentadecylic acid  Table 4, Supplementary Fig. 2) revealed 6 distinct FA groups with strong and statistically significant intra-group correlations (r p > 0.4, often > 0.7-0.8). The same FA group structure was confirmed to a very large extent using unsupervised HC (Fig. 7). Further, the FA groups were biologically relevant with, for instance, group 2 being mostly formed of C14-16 SFAs. Clustering of the sample types or the diagnoses of the horses was only partially achieved (Fig. 7, top rows), in agreement with DA cross-validation.

Random forest tree analyses of FA profiles.
To estimate the predicting power of FA profiles, a RF analysis was performed. In RF-based classification of the samples' inflammatory state (as diagnosed), the 6 FA groups got different importance scores ( Supplementary Fig. 3), and as a positive control, RF perfectly predicted samples' diagnosis when based on immune cells-%, predominantly neutrophil-and macrophage-% (Supplementary Fig. 4). When selecting randomly the samples from the enriched dataset into training and testing sets, the approach yielded 99.99% prediction accuracy on the test data, demonstrating that the subsets of the full FA dataset included for training were sufficient to predict other FA subsets from the same samples, preserving the information from the full dataset. This result emphasized the relevance of the FA groups, as it demonstrated that they contained redundant information. When the grouped FA data were enriched after splitting into training and testing samples, the prediction accuracy was 40-59% depending on sample type and whether derived variables were excluded or included (Supplementary Table 5). FA groups 1 (16:1n-9, 18:2n-6, and 18:3n-3) and 2 (14:0, 15:0, 16:0, and 16:1n-7) were most frequently relevant to the RF model outcomes when all sample types were analyzed together (Supplementary Table 4, Supplementary Fig. 3), or samples were taken from EVs only (data not shown). However, when restricted to BALF samples, the most important features were Groups 1 and 6 (that includes 20:3n-6). These differences in the importance for diagnosis classification of different FA groups were even more apparent when the abundance of individual FAs was used for classification ( Supplementary Figs. 5, 6), with 20:3n-6 as the topmost feature for BALF samples, in agreement with other analyses of this study.

Discussion
This study investigated the pathophysiology of EA by describing the FA profiles of equine BALF and its components, and FA alterations that reflect the airway inflammatory status of asthmatic horses. This study demonstrates differences in FA profiles of different BALF components and between horses with EA and healthy horses. The potential role of BALF EVs as carriers of FAs with impact on the progression and resolution of inflammation and airway dysfunction is supported by the results. The differences in FA profiles between EA and healthy horses are not sufficient for diagnostics tools, however, the FA features reflect the inflammatory status of the animals. The horses in this study had naturally occurring disease, which improves the quality of this translational animal model and applicability of the results to human patients. The enrolled horses in our study epitomized a natural horse population where MMEA is more prevalent compared to SEA. However, significant overlap in terms of EA severity-based subcategories exists and a clear-cut difference cannot be expected in the biochemical markers of inflammation. The clinical status of EA horses also affects the lipid composition of the airways, as shown by Christmann et al. 33 , where cyclic phosphatidic acid 16:0 and diacylglycerol 36:2 were elevated in the surfactant of EA horses during induced disease exacerbation. There is still no consensus if MMEA and SEA represent two distinct diseases or if they belong to the same continuum 2 . In this study, the FA profiles of three different sample types were analyzed. BALF contains cells and molecules, such as proteins and lipids, while SUP represents cell-free BALF, and the EVs contain the SEC-purified EV fraction. The MMEA and SEA samples showed distinct FA compositions when compared to healthy horses. Only three FAs (20:0, 20:1n-7, and 20:3n-6) showed elevated proportions in asthma samples in all three sample types. Thus, mainly different individual FAs were responsible for the discrimination of the diagnoses in BALF, SUP, and EVs. These distinctive FA profiles indicate differences in FA transfer from cells into the surrounding fluid and EVs, and that EVs might serve as a targeted delivery system and carry FAs that potentially modulate immune functions.
The supervised DA yielded good separation power when the diagnosis of each sample was known. In the absence of any a priori knowledge, a PCA-based SIMCA and leave-one-out DA were also performed. With these unsupervised analyses, the clearest separation of healthy and SEA horses was in the EV fraction. The ability of RF classification to predict the diagnosis based on FA profiles was approximately 60% at its best. This limited www.nature.com/scientificreports/ performance might originate from the small number of animals in the study, and it would be informative to repeat similar approaches on larger datasets. However, as explained above, asthma is not a disease that can always be diagnosed in a clear-cut manner, and therefore the 60% classification of the unsupervised methods was not unexpected. Moreover, the main aim of the study was not to diagnose asthma with FA profiles, but to assess the significance of FAs in disease pathogenesis and prediction. Surfactant facilitates lung function by reducing tension and preventing the alveoli and small airways from collapsing, hence the impaired activity of surfactant complicates the function of the airways 34 . Previous research as far as from three decades ago revealed altered surfactant aggregate and protein composition as well as surfactant dysfunction in humans with allergen challenge 35 . Phosphatidylcholine 16:0/16:0 is the main phospholipid species of surfactant 36 . Christmann et al. 37 found lower concentrations of surfactant phospholipids in cell-free BALF and surfactant pellet in EA horses in remission and exacerbation compared to control horses. In our study, the decreased 16:0 and total SFAs in EVs in SEA suggest that EVs could have a role in the transfer of FAs crucial to surfactant function. However, another study on EA horses observed that cell-free surfactant pellets of SEA individuals had increased levels of sphingolipids with 16:0 acyl chain and cyclic phosphatidic acid 16:0 23 .   712  723  488  492  495  716  489  490  819  727  823  726  795  491  721  725  772  718  724  713  825  487  717  807  806  714  765  715  766  497  719  814  796  809  798  498  720  494  812  822  794  805  496  768  770  771  493  824  769  810  804  767  787  773  792  774  802  776  779  777  813  784  818  782  817  778  786  775  788  789  808  781  793  791  797  722  790  728  803  801  816  780  783  811  820  815  785  799  Gp. 5 Gp. 4 Gp. 4 Gp. 4 Gp. 6 Gp. 6 Gp. 6 Gp. 6 Gp. 3 Gp. 3 www.nature.com/scientificreports/ Our results revealed an increase in BALF 18:0 mol-% in SEA horses. Evidence of inflammatory actions of SFAs has emerged in human asthma, where lysophosphatidylcholine 18:0 and 16:0 were elevated in BALF of patients with lung function impairment characteristic of asthma 38 . On the other hand, increased SFAs, such as 18:0, might also indicate the presence of plasma lipoproteins in BALF, caused by leakage from capillaries to alveoli in asthma 34 . In general, the BALF, SUP, and EVs of asthmatic horses contained elevated relative amounts of C20-24 SFAs, characteristic to lung sphingolipids 39 . Also Sánchez-Rodríguez et al. 40 found an increase in SFA 22:0 in erythrocytes of human asthma patients, and reported that 24:0 could be a useful biomarker of lung cancer. Since induction of membrane rafts rich in sphingolipids (ceramide, sphingomyelin, and complex glycosphingolipids) has been associated with vesicle budding 41 , asthma may modulate vesiculation in the horse lung. Due to vesicle interleaflet coupling by acyl chain digitation, an increase in outer leaflet sphingolipids having C20-24 acyl chains also necessitates an elevation in inner leaflet phosphatidylserine 18:0/18:1, thus, also increasing the need for C18 FAs 42 . In our study, the EVs were enriched with 18:1n-9, resembling the findings from earlier studies where MUFAs, especially 18:1, were elevated in the FA profile of EVs from human prostate cancer cells 43 and fibroblast-like synoviocytes 44 . Hough et al. 1 also documented increased sphingomyelin species 34:1 (consisting of a 18:1 sphingoid base and 16:0 acyl chain) in BALF EVs of smoke-exposed asthmatic patients.
In the present study, 20:4n-6, which regulates immune responses in hypersensitivity reactions and serves as a precursor for eicosanoids 45 , increased in the SUP samples of pooled EA horses. Eicosanoids from 20:4n-6, such as prostaglandin E 2 (PGE 2 ) and cysteinyl leukotrienes, are bioactive lipid mediators primarily associated with inflammatory conditions and pain 46 . In the respiratory system, however, PGE 2 has beneficial effects, as it increases relaxation of airway smooth muscle and inhibits the release of mast cell mediators and the recruitment of inflammatory cells 13 . Cysteinyl leukotrienes, on the other hand, play a consequential role in human asthma as potent bronchoconstrictors 47 . Derivatives of 20:4n-6 also include prostaglandins and lipoxins (early resolution SPMs) that can have pro-resolving actions and thereby attenuate inflammatory responses, impede tissue remodeling, and inhibit bronchoconstriction 13,48,49 . The function of 20:4n-6 is complex, however, in our study its increase in the SUP of asthmatic horses could be linked to the chronic inflammation present in the airways.
We observed an inverse association between 18:2n-6 and neutrophil-% in BALF and EV samples, and a similar phenomenon was documented by Sánchez-Rodríguez et al. 40 , who reported that erythrocyte 18:2n-6 decreased in patients with lung adenocarcinoma and squamous cell lung carcinoma. The other essential PUFA, 18:3n-3, also showed a negative association with BALF inflammation. A potential reason for the inflammation-related decrease in the essential PUFAs could be their intensified elongation and desaturation to lipid mediator precursors 50 , such as 20:3n-6 and 20:5n-3, that increased in SEA EVs. In addition to alterations in pro-inflammatory PUFAs, also those with known anti-inflammatory actions, such as 20:5n-3 and conjugated 18:2c9,t11 51 , increased in proportion in EV samples and/or BALF by EA. The increase in n-3 PUFAs may be beneficial in the resolving phase of inflammation, as cells can convert them to SPMs, such as resolvins, protectins, and maresins, in order to restore the homeostasis in the airways during inflammation 14 .
The proportions of 20:3n-6 increased in all sample types in EA. Woods et al. 52 identified a positive association in young adults' plasma 20:3n-6 levels and asthma, and increased proportions were noticed in the synovial membrane of patients with rheumatoid arthritis 53 , suggesting a crucial role for 20:3n-6 in chronic inflammatory conditions. 20:3n-6 is also the precursor of the anti-inflammatory prostaglandin E 1 (PGE 1 ), which promotes vasodilation, lowers blood pressure, and relaxes smooth muscle and, thereby, has the potential to improve lung function 54,55 . An in vitro study by Martin et al. 56 demonstrated anti-inflammatory effects of the PGE 1 analog, misoprostol, on equine blood neutrophils, mediated through inhibited neutrophil adhesion and migration in a dose-dependent manner. Our FA results suggest that, in addition to sustaining PUFA-mediated inflammatory processes, asthmatic lungs could simultaneously induce compensatory mechanisms to ameliorate inflammation and bronchoconstriction.
The present results show that, in horses with naturally occurring asthma, airway inflammation is associated with altered FA profiles in all examined BALF components, which indicates that the asthmatic airways react to the situation by altered FA proportions with both pro-and anti-inflammatory effects. Although the different sample types showed unique responses of FA composition to asthma, the elevated 20:3n-6 was an asthma marker common to all sample types. In SEA horses, EVs also had increased proportions of the anti-inflammatory 20:5n-3, and decreased proportions of the main surfactant FA, 16:0. The supervised DA method separated samples based on diagnoses excellently, while the ability of RF classification to predict the diagnosis based on FA profiles remained insufficient for diagnostics. Based on PCA-SIMCA, however, the FA profile of EVs was distinctly different between healthy and SEA horses indicating EVs as potential mediators in asthma pathogenesis. In this study, the SFA and PUFA components of EVs that responded to asthma are known to play central roles in inflammatory signaling pathways. Thus, the FA results provide new insight to the development and possible treatment of EA. Future studies should investigate the EV FA profiles in different inflammatory phases, in order to explore the potential of EVs as carriers of particular anti-inflammatory FAs as a part of asthma therapy.

Study limitations.
This study focused on FAs instead of neutral or phospholipids, therefore, the lipid class profiles of EV membranes in naturally occurring EA remain to be investigated. Blood contamination of BALF (a potential source of increased lipoprotein concentration) is possible but unlikely in this study, as no red blood cells were detected in the samples. In addition, SEC as a purification method for EVs may not remove all lipoprotein and protein residues from the samples, which can affect the EV FA profiles 57 . Finally, the individual diets of the horses could have caused variation in FA profiles, and the limited number of animals could have reduced the predicting value of FAs.